Integrating visual and inertial data as visual-inertial odometry (VIO) is a well-defined problem in mobile robotics.
Both cameras and inertial sensors are relatively low-cost, power-efficient and widely deployed. These two sensors are complementary: monocular cameras capture the appearance and structure of a 3D scene, while they are scale-ambiguous, and not robust to challenging scenarios, e.g. strong lighting changes, lack of texture and high-speed motion;
In contrast, IMUs are completely ego-centric, scene-independent, and can also provide absolute metric scale. Nevertheless, the downside is that inertial measurements, especially from low-cost devices, are plagued by process noise and biases. An effective fusion of the measurements from these two complementary sensors is of key importance to accurate pose estimation. Thus, according to their information fusion methods, conventional model based visual-inertial approaches are roughly segmented into three different classes :
filtering approaches [1]}, fixed-lag smoothers [2]} and full smoothing methods [3]}.
